R generation

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x

1 R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

1.1 R-project

https://cran.r-project.org/

1.2 RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

1.2.1 RStudio eklentileri

Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/

devtools::install_github("rstudio/addinexamples", type = "source")

Skipping install of 'addinexamples' from a github remote, the SHA1 (fae96091) has not changed since last install.
  Use `force = TRUE` to force installation

1.3 X11

https://www.xquartz.org/

1.4 Java OS

https://support.apple.com/kb/dl1572

2 R paketleri

2.1 Neden paketler var

I love the #rstats community.
Someone is like, “oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It's called pbplyr.”
What a tribe.
— Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) July 3, 2018

https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

2.2 Paketleri nereden bulabiliriz

Available CRAN Packages By Name https://cran.r-project.org/web/packages/available_packages_by_name.html
Bioconductor https://www.bioconductor.org

2.3 R için yardım bulma

?mean
??efetch

Vignette

RDocumentation https://www.rdocumentation.org
R Package Documentation https://rdrr.io/
GitHub
Stackoverflow

https://stackoverflow.com/

Google uygun anahtar kelime

How I use #rstats
h/t @ThePracticalDev pic.twitter.com/erRnTG0Ujr
— Emily Bovee (@ebovee09) August 10, 2018

Awesome Cheatsheet https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/

Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/

Twitter

https://twitter.com/hashtag/rstats?src=hash

2.4 R paket yükleme

install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")

# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")

require(tidyverse)
require(jmv)
require(questionr)
library(summarytools)

3 R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

4 RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

4.1 Excel

4.2 SPSS

4.3 csv

5 Veriyi görüntüleme

Spreadsheet users using #rstats: where's the data?#rstats users using spreadsheets: where's the code?
— Leonard Kiefer (@lenkiefer) July 7, 2018

library(nycflights13)
summary(flights)

      year          month             day       
 Min.   :2013   Min.   : 1.000   Min.   : 1.00  
 1st Qu.:2013   1st Qu.: 4.000   1st Qu.: 8.00  
 Median :2013   Median : 7.000   Median :16.00  
 Mean   :2013   Mean   : 6.549   Mean   :15.71  
 3rd Qu.:2013   3rd Qu.:10.000   3rd Qu.:23.00  
 Max.   :2013   Max.   :12.000   Max.   :31.00  
                                                
    dep_time    sched_dep_time   dep_delay      
 Min.   :   1   Min.   : 106   Min.   : -43.00  
 1st Qu.: 907   1st Qu.: 906   1st Qu.:  -5.00  
 Median :1401   Median :1359   Median :  -2.00  
 Mean   :1349   Mean   :1344   Mean   :  12.64  
 3rd Qu.:1744   3rd Qu.:1729   3rd Qu.:  11.00  
 Max.   :2400   Max.   :2359   Max.   :1301.00  
 NA's   :8255                  NA's   :8255     
    arr_time    sched_arr_time   arr_delay       
 Min.   :   1   Min.   :   1   Min.   : -86.000  
 1st Qu.:1104   1st Qu.:1124   1st Qu.: -17.000  
 Median :1535   Median :1556   Median :  -5.000  
 Mean   :1502   Mean   :1536   Mean   :   6.895  
 3rd Qu.:1940   3rd Qu.:1945   3rd Qu.:  14.000  
 Max.   :2400   Max.   :2359   Max.   :1272.000  
 NA's   :8713                  NA's   :9430      
   carrier              flight       tailnum         
 Length:336776      Min.   :   1   Length:336776     
 Class :character   1st Qu.: 553   Class :character  
 Mode  :character   Median :1496   Mode  :character  
                    Mean   :1972                     
                    3rd Qu.:3465                     
                    Max.   :8500                     
                                                     
    origin              dest              air_time    
 Length:336776      Length:336776      Min.   : 20.0  
 Class :character   Class :character   1st Qu.: 82.0  
 Mode  :character   Mode  :character   Median :129.0  
                                       Mean   :150.7  
                                       3rd Qu.:192.0  
                                       Max.   :695.0  
                                       NA's   :9430   
    distance         hour           minute     
 Min.   :  17   Min.   : 1.00   Min.   : 0.00  
 1st Qu.: 502   1st Qu.: 9.00   1st Qu.: 8.00  
 Median : 872   Median :13.00   Median :29.00  
 Mean   :1040   Mean   :13.18   Mean   :26.23  
 3rd Qu.:1389   3rd Qu.:17.00   3rd Qu.:44.00  
 Max.   :4983   Max.   :23.00   Max.   :59.00  
                                               
   time_hour                  
 Min.   :2013-01-01 05:00:00  
 1st Qu.:2013-04-04 13:00:00  
 Median :2013-07-03 10:00:00  
 Mean   :2013-07-03 05:22:54  
 3rd Qu.:2013-10-01 07:00:00  
 Max.   :2013-12-31 23:00:00

View(data)

data

head

tail

glimpse

str

skimr::skim()

6 Veriyi değiştirme

6.1 Veriyi kod ile değiştirelim

6.2 Veriyi eklentilerle değiştirme

6.3 RStudio aracılığıyla recode

questionr paketi kullanılacak

https://juba.github.io/questionr/articles/recoding_addins.html

7 Basit tanımlayıcı istatistikler

summary()

mean

median

min

max

sd

table()

library(readr)
irisdata <- read_csv("data/iris.csv")

Parsed with column specification:
cols(
  Sepal.Length = col_double(),
  Sepal.Width = col_double(),
  Petal.Length = col_double(),
  Petal.Width = col_double(),
  Species = col_character()
)

jmv::descriptives(
    data = irisdata,
    vars = "Sepal.Length",
    splitBy = "Species",
    freq = TRUE,
    hist = TRUE,
    dens = TRUE,
    bar = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sum = TRUE,
    sd = TRUE,
    variance = TRUE,
    range = TRUE,
    se = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE,
    pcEqGr = TRUE)


 DESCRIPTIVES

 Descriptives                                          
 ───────────────────────────────────────────────────── 
                          Species       Sepal.Length   
 ───────────────────────────────────────────────────── 
   N                      setosa                  50   
                          versicolor              50   
                          virginica               50   
   Missing                setosa                   0   
                          versicolor               0   
                          virginica                0   
   Mean                   setosa                5.01   
                          versicolor            5.94   
                          virginica             6.59   
   Std. error mean        setosa              0.0498   
                          versicolor          0.0730   
                          virginica           0.0899   
   Median                 setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   Mode                   setosa                5.00   
                          versicolor            5.50   
                          virginica             6.30   
   Sum                    setosa                 250   
                          versicolor             297   
                          virginica              329   
   Standard deviation     setosa               0.352   
                          versicolor           0.516   
                          virginica            0.636   
   Variance               setosa               0.124   
                          versicolor           0.266   
                          virginica            0.404   
   Range                  setosa                1.50   
                          versicolor            2.10   
                          virginica             3.00   
   Minimum                setosa                4.30   
                          versicolor            4.90   
                          virginica             4.90   
   Maximum                setosa                5.80   
                          versicolor            7.00   
                          virginica             7.90   
   Skewness               setosa               0.120   
                          versicolor           0.105   
                          virginica            0.118   
   Std. error skewness    setosa               0.337   
                          versicolor           0.337   
                          virginica            0.337   
   Kurtosis               setosa              -0.253   
                          versicolor          -0.533   
                          virginica           0.0329   
   Std. error kurtosis    setosa               0.662   
                          versicolor           0.662   
                          virginica            0.662   
   25th percentile        setosa                4.80   
                          versicolor            5.60   
                          virginica             6.23   
   50th percentile        setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   75th percentile        setosa                5.20   
                          versicolor            6.30   
                          virginica             6.90   
 ─────────────────────────────────────────────────────

# install.packages("scatr")
scatr::scat(
    data = irisdata,
    x = "Sepal.Length",
    y = "Sepal.Width",
    group = "Species",
    marg = "dens",
    line = "linear",
    se = TRUE)

7.1 summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

# library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")

7.1.1 Frequencies

Variable: iris$Species
Type: Factor (unordered)

	Freq	% Valid	% Valid Cum.	% Total	% Total Cum.
setosa	50	33.33	33.33	33.33	33.33
versicolor	50	33.33	66.67	33.33	66.67
virginica	50	33.33	100.00	33.33	100.00
<NA>	0			0.00	100.00
Total	150	100.00	100.00	100.00	100.00

freq(iris$Species, report.nas = FALSE, style = "rmarkdown", omit.headings = TRUE)

	Freq	%	% Cum.
setosa	50	33.33	33.33
versicolor	50	33.33	66.67
virginica	50	33.33	100.00
Total	150	100.00	100.00

with(tobacco, print(ctable(smoker, diseased), method = 'render'))

Cross-Tabulation / Row Proportions

Variables: smoker * diseased
Data Frame: tobacco

	diseased
smoker	Yes	No	Total
Yes	125 (41.95%)	173 (58.05%)	298 (100.00%)
No	99 (14.10%)	603 (85.90%)	702 (100.00%)
Total	224 (22.40%)	776 (77.60%)	1000 (100.00%)

Generated by summarytools 0.8.5 (R version 3.5.1)
2018-07-21

with(tobacco, 
     print(ctable(smoker, diseased, prop = 'n', totals = FALSE), 
           omit.headings = TRUE, method = "render"))

	diseased
smoker	Yes	No
Yes	125	173
No	99	603

Generated by summarytools 0.8.5 (R version 3.5.1)
2018-07-21

summarytools::descr(iris, style = "rmarkdown")

Non-numerical variable(s) ignored: Species ### Descriptive Statistics
Data Frame: iris
N: 150

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
Mean	5.84	3.06	3.76	1.20
Std.Dev	0.83	0.44	1.77	0.76
Min	4.30	2.00	1.00	0.10
Q1	5.10	2.80	1.60	0.30
Median	5.80	3.00	4.35	1.30
Q3	6.40	3.30	5.10	1.80
Max	7.90	4.40	6.90	2.50
MAD	1.04	0.44	1.85	1.04
IQR	1.30	0.50	3.50	1.50
CV	7.06	7.01	2.13	1.57
Skewness	0.31	0.31	-0.27	-0.10
SE.Skewness	0.20	0.20	0.20	0.20
Kurtosis	-0.61	0.14	-1.42	-1.36
N.Valid	150.00	150.00	150.00	150.00
Pct.Valid	100.00	100.00	100.00	100.00

descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
      omit.headings = TRUE, style = "rmarkdown")

Non-numerical variable(s) ignored: Species

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.84	0.83	4.30	5.80	7.90
Sepal.Width	3.06	0.44	2.00	3.00	4.40
Petal.Length	3.76	1.77	1.00	4.35	6.90
Petal.Width	1.20	0.76	0.10	1.30	2.50

view(dfSummary(iris))

dfSummary(tobacco, plain.ascii = FALSE, style = "grid")

7.1.2 Data Frame Summary

tobacco
N: 1000

No	Variable	Stats / Values	Freqs (% of Valid)	Text Graph	Valid	Missing
1	gender [factor]	1. F 2. M	489 (50.0%) 489 (50.0%)	IIIIIIIIIIIIIIII IIIIIIIIIIIIIIII	978 (97.8%)	22 (2.2%)
2	age [numeric]	mean (sd) : 49.6 (18.29) min < med < max : 18 < 50 < 80 IQR (CV) : 32 (0.37)	63 distinct val.		975 (97.5%)	25 (2.5%)
3	age.gr [factor]	1. 18-34 2. 35-50 3. 51-70 4. 71 +	258 (26.5%) 241 (24.7%) 317 (32.5%) 159 (16.3%)	IIIIIIIIIIIII IIIIIIIIIIII IIIIIIIIIIIIIIII IIIIIIII	975 (97.5%)	25 (2.5%)
4	BMI [numeric]	mean (sd) : 25.73 (4.49) min < med < max : 8.83 < 25.62 < 39.44 IQR (CV) : 5.72 (0.17)	974 distinct val.		974 (97.4%)	26 (2.6%)
5	smoker [factor]	1. Yes 2. No	298 (29.8%) 702 (70.2%)	IIIIII IIIIIIIIIIIIIIII	1000 (100%)	0 (0%)
6	cigs.per.day [numeric]	mean (sd) : 6.78 (11.88) min < med < max : 0 < 0 < 40 IQR (CV) : 11 (1.75)	37 distinct val.		965 (96.5%)	35 (3.5%)
7	diseased [factor]	1. Yes 2. No	224 (22.4%) 776 (77.6%)	IIII IIIIIIIIIIIIIIII	1000 (100%)	0 (0%)
8	disease [character]	1. Hypertension 2. Cancer 3. Cholesterol 4. Heart 5. Pulmonary 6. Musculoskeletal 7. Diabetes 8. Hearing 9. Digestive 10. Hypotension [ 3 others ]	36 (16.2%) 34 (15.3%) 21 ( 9.5%) 20 ( 9.0%) 20 ( 9.0%) 19 ( 8.6%) 14 ( 6.3%) 14 ( 6.3%) 12 ( 5.4%) 11 ( 5.0%) 21 ( 9.4%)	IIIIIIIIIIIIIIII IIIIIIIIIIIIIII IIIIIIIII IIIIIIII IIIIIIII IIIIIIII IIIIII IIIIII IIIII IIII IIIIIIIII	222 (22.2%)	778 (77.8%)
9	samp.wgts [numeric]	mean (sd) : 1 (0.08) min < med < max : 0.86 < 1.04 < 1.06 IQR (CV) : 0.19 (0.08)	0.86!: 267 (26.7%) 1.04!: 249 (24.9%) 1.05!: 324 (32.4%) 1.06!: 160 (16.0%) ! rounded	IIIIIIIIIIIII IIIIIIIIIIII IIIIIIIIIIIIIIII IIIIIII	1000 (100%)	0 (0%)

# First save the results
iris_stats_by_species <- by(data = iris, 
                            INDICES = iris$Species, 
                            FUN = descr, stats = c("mean", "sd", "min", "med", "max"), 
                            transpose = TRUE)
# Then use view(), like so:
view(iris_stats_by_species, method = "pander", style = "rmarkdown")

7.1.3 Descriptive Statistics

Data Frame: iris
Group: Species = setosa
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.01	0.35	4.30	5.00	5.80
Sepal.Width	3.43	0.38	2.30	3.40	4.40
Petal.Length	1.46	0.17	1.00	1.50	1.90
Petal.Width	0.25	0.11	0.10	0.20	0.60

Group: Species = versicolor
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.94	0.52	4.90	5.90	7.00
Sepal.Width	2.77	0.31	2.00	2.80	3.40
Petal.Length	4.26	0.47	3.00	4.35	5.10
Petal.Width	1.33	0.20	1.00	1.30	1.80

Group: Species = virginica
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	6.59	0.64	4.90	6.50	7.90
Sepal.Width	2.97	0.32	2.20	3.00	3.80
Petal.Length	5.55	0.55	4.50	5.55	6.90
Petal.Width	2.03	0.27	1.40	2.00	2.50

view(iris_stats_by_species)

Output file written: /var/folders/76/rq_s_23s7fd5r8hqrbg8rmnc0000gp/T//RtmpxIdFKz/file8d943e622540.html

Group: Species = versicolor
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	5.94	0.52	4.90	5.90	7.00
Sepal.Width	2.77	0.31	2.00	2.80	3.40
Petal.Length	4.26	0.47	3.00	4.35	5.10
Petal.Width	1.33	0.20	1.00	1.30	1.80

Group: Species = virginica
N: 50

	Mean	Std.Dev	Min	Median	Max
Sepal.Length	6.59	0.64	4.90	6.50	7.90
Sepal.Width	2.97	0.32	2.20	3.00	3.80
Petal.Length	5.55	0.55	4.50	5.55	6.90
Petal.Width	2.03	0.27	1.40	2.00	2.50

Generated by summarytools 0.8.6 (R version 3.5.1)
2018-07-21

Output file appended: /var/folders/76/rq_s_23s7fd5r8hqrbg8rmnc0000gp/T//RtmpxIdFKz/file8d943e622540.html

data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr, 
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")

7.1.4 Descriptive Statistics

Variable: tobacco$BMI by age.gr

	18-34	35-50	51-70	71 +
Mean	23.84	25.11	26.91	27.45
Std.Dev	4.23	4.34	4.26	4.37
Min	8.83	10.35	9.01	16.36
Median	24.04	25.11	26.77	27.52
Max	34.84	39.44	39.21	38.37

BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr,  transpose = TRUE,
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)

	Mean	Std.Dev	Min	Median	Max
18-34	23.84	4.23	8.83	24.04	34.84
35-50	25.11	4.34	10.35	25.11	39.44
51-70	26.91	4.26	9.01	26.77	39.21
71 +	27.45	4.37	16.36	27.52	38.37

tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)
view(freq_tables, footnote = NA, file = 'freq-tables.html')

Output file written: freq-tables.html

tobacco_subset$age.gr

Type: Factor (unordered)

		Valid		Total
age.gr	Freq	%	% Cumul	%	% Cumul
18-34	258	26.46	26.46	25.80	25.80
35-50	241	24.72	51.18	24.10	49.90
51-70	317	32.51	83.69	31.70	81.60
71 +	159	16.31	100.00	15.90	97.50
<NA>	25			2.50	100.00
Total	1000	100.00	100.00	100.00	100.00

tobacco_subset$smoker

Type: Factor (unordered)

		Valid		Total
smoker	Freq	%	% Cumul	%	% Cumul
Yes	298	29.80	29.80	29.80	29.80
No	702	70.20	100.00	70.20	100.00
<NA>	0			0.00	100.00
Total	1000	100.00	100.00	100.00	100.00

Output file appended: freq-tables.html

what.is(iris)

function ‘is’ appears not to be S3 generic; found functions that look like S3 methods‘>=’ not meaningful for factors$properties

$attributes.lengths names class row.names 5 1 150

$extensive.is [1] “is.data.frame” “is.list”
[3] “is.object” “is.recursive” [5] “is.unsorted”

freq(tobacco$gender, style = 'rmarkdown')

### Frequencies   
**Variable:** tobacco$gender     
**Type:** Factor (unordered)   

|     &nbsp; | Freq | % Valid | % Valid Cum. | % Total | % Total Cum. |
|-----------:|-----:|--------:|-------------:|--------:|-------------:|
|      **F** |  489 |   50.00 |        50.00 |   48.90 |        48.90 |
|      **M** |  489 |   50.00 |       100.00 |   48.90 |        97.80 |
| **\<NA\>** |   22 |         |              |    2.20 |       100.00 |
|  **Total** | 1000 |  100.00 |       100.00 |  100.00 |       100.00 |

print(freq(tobacco$gender), method = 'render')

Frequencies

Variable: gender
Type: Factor (unordered)

		Valid		Total
gender	Freq	%	% Cumul	%	% Cumul
F	489	50.00	50.00	48.90	48.90
M	489	50.00	100.00	48.90	97.80
<NA>	22			2.20	100.00
Total	1000	100.00	100.00	100.00	100.00

Generated by summarytools 0.8.6 (R version 3.5.1)
2018-07-21

7.2 Grafikler

library(ggplot2)
library(mosaic)

Zorunlu paket yükleniyor: lattice
Zorunlu paket yükleniyor: ggformula

New to ggformula?  Try the tutorials: 
    learnr::run_tutorial("introduction", package = "ggformula")
    learnr::run_tutorial("refining", package = "ggformula")
Zorunlu paket yükleniyor: mosaicData
Zorunlu paket yükleniyor: Matrix

Attaching package: ‘Matrix’

The following object is masked from ‘package:tidyr’:

    expand


The 'mosaic' package masks several functions from core packages in order to add 
additional features.  The original behavior of these functions should not be affected by this.

Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.

Attaching package: ‘mosaic’

The following object is masked from ‘package:Matrix’:

    mean

The following object is masked from ‘package:questionr’:

    prop

The following objects are masked from ‘package:dplyr’:

    count, do, tally

The following object is masked from ‘package:purrr’:

    cross

The following object is masked from ‘package:ggplot2’:

    stat

The following objects are masked from ‘package:stats’:

    binom.test, cor, cor.test, cov, fivenum, IQR,
    median, prop.test, quantile, sd, t.test, var

The following objects are masked from ‘package:base’:

    max, mean, min, prod, range, sample, sum

mPlot(irisdata)

Choose a plot type. 

1: 1-variable (histogram, density plot, etc.)
2: 2-variable (scatter, boxplot, etc.)
3: map

ctable(tobacco$gender, tobacco$smoker, style = 'rmarkdown')

### Cross-Tabulation / Row Proportions   
**Variables:** gender * smoker     
**Data Frame:** tobacco   
   
|        |        |              |              |                |
|-------:|-------:|-------------:|-------------:|---------------:|
|        | smoker |          Yes |           No |          Total |
| gender |        |              |              |                |
|      F |        | 147 (30.06%) | 342 (69.94%) |  489 (100.00%) |
|      M |        | 143 (29.24%) | 346 (70.76%) |  489 (100.00%) |
| \<NA\> |        |   8 (36.36%) |  14 (63.64%) |   22 (100.00%) |
|  Total |        | 298 (29.80%) | 702 (70.20%) | 1000 (100.00%) |

print(ctable(tobacco$gender, tobacco$smoker), method = 'render')

descr(tobacco, style = ‘rmarkdown’)

print(descr(tobacco), method = ‘render’, table.classes = ‘st-small’)

dfSummary(tobacco, style = ‘grid’, plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = ‘render’)

Here, building up a #ggplot2 as slowly as possible, #rstats. Incremental adjustments. #rstatsteachingideas pic.twitter.com/nUulQl8bPh
— Gina Reynolds (@EvaMaeRey) August 13, 2018

Dreaming of a fancy #Rstats #ggplot #dataviz but still scared of typing #code? @_pvictorr esquisse package has you covered https://t.co/1vIDXcVAAF pic.twitter.com/RlTkptnrNv
— Radoslaw Panczak (@RPanczak) October 2, 2018

8 Rcmdr

library(Rcmdr)

Rcmdr::Commander()

A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/

9 jamovi

https://www.jamovi.org/

https://blog.jamovi.org/2018/07/30/rj.html

10 Sonraki Konular

RStudio ile GitHub
Hipotez testleri
R Markdown ve R Notebook ile tekrarlanabilir rapor

11 Diğer kodlar

Diğer kodlar için bakınız: https://sbalci.github.io/

12 Geri Bildirim

Geri bildirim için tıklayınız: Geri bildirim formu

Bu bir derlemedir, mümkün mertebe alıntılara referans vermeye çalıştım.↩

---
title: R ile analize başlarken^[Bu bir derlemedir, mümkün mertebe alıntılara referans
  vermeye çalıştım.]
author: "Derleyen [Serdar Balcı, MD, Pathologist](https://sbalci.github.io/)"
date: "`r format(Sys.Date())`"
output:
  html_notebook:
    fig_caption: yes
    highlight: kate
    number_sections: yes
    theme: flatly
    toc: yes
    toc_depth: 5
    toc_float: yes
  pdf_document:
    toc: yes
    toc_depth: '5'
  html_document:
    df_print: paged
    toc: yes
    toc_depth: '5'
---


[![](http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530113077/Image_2_vfy48b.png)](https://www.datacamp.com/community/tutorials/data-science-pitfalls)


- R generation

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x


# R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

[![What is R?](http://img.youtube.com/vi/XcBLEVknqvY/0.jpg)](http://www.youtube.com/watch?v=XcBLEVknqvY)


## R-project

https://cran.r-project.org/

---

[![](https://ismayc.github.io/talks/ness-infer/img/engine.png)](https://ismayc.github.io/talks/ness-infer/slide_deck.html#6)

---

## RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

---

### RStudio eklentileri

- Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/

```{r}
devtools::install_github("rstudio/addinexamples", type = "source")
```


---

## X11

https://www.xquartz.org/

---

## Java OS

https://support.apple.com/kb/dl1572

---

# R paketleri


## Neden paketler var

[![](https://ismayc.github.io/talks/ness-infer/img/appstore.png)](https://ismayc.github.io/talks/ness-infer/slide_deck.html#7)

---

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">I love the <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> community.<br>Someone is like, &quot;oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It&#39;s called pbplyr.&quot;<br>What a tribe.</p>&mdash; Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) <a href="https://twitter.com/Frankly_Data/status/1014189095294291968?ref_src=twsrc%5Etfw">July 3, 2018</a></blockquote>

---



https://blog.mitchelloharawild.com/blog/user-2018-feature-wall/

---

![](https://blog.mitchelloharawild.com/blog/2018-07-11-user-2018-feature-wall_files/final.jpg)

---

## Paketleri nereden bulabiliriz

- Available CRAN Packages By Name
https://cran.r-project.org/web/packages/available_packages_by_name.html

- Bioconductor
https://www.bioconductor.org


---

## R için yardım bulma


```{r yardım}
?mean
??efetch
```



- Vignette

![](figures/vignette.png)

---

- RDocumentation
https://www.rdocumentation.org

- R Package Documentation
https://rdrr.io/

- GitHub

- Stackoverflow

https://stackoverflow.com/

- Google uygun anahtar kelime



<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">How I use <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> <br>h/t <a href="https://twitter.com/ThePracticalDev?ref_src=twsrc%5Etfw">@ThePracticalDev</a> <a href="https://t.co/erRnTG0Ujr">pic.twitter.com/erRnTG0Ujr</a></p>&mdash; Emily Bovee (@ebovee09) <a href="https://twitter.com/ebovee09/status/1028037594947485696?ref_src=twsrc%5Etfw">August 10, 2018</a></blockquote>


---


![](figures/Google-package-name.png)

---



![](figures/Google-start-with-R.png)

---

- Awesome Cheatsheet
https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/


- Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/




- Twitter

https://twitter.com/hashtag/rstats?src=hash


---

## R paket yükleme

```
install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")
```

```{r paket yükleme}
# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools")
```


```{r paket çağırma, error=FALSE, message = FALSE, warning = FALSE, eval = TRUE, include = TRUE}
require(tidyverse)
require(jmv)
require(questionr)
library(summarytools)
```

---

# R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

![](http://www.rstudio.com/images/docs/projects_new.png)

---

# RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

![](https://support.rstudio.com/hc/en-us/article_attachments/206277618/data-import-overview.gif)

---

## Excel

## SPSS

## csv


---

# Veriyi görüntüleme

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Spreadsheet users using <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a>:  where&#39;s the data?<a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> users using spreadsheets:  where&#39;s the code?</p>&mdash; Leonard Kiefer (@lenkiefer) <a href="https://twitter.com/lenkiefer/status/1015587475580956672?ref_src=twsrc%5Etfw">July 7, 2018</a></blockquote>



```{r, results="markup"}
library(nycflights13)
summary(flights)
```



```
View(data)
```


```
data
```


```
head
```


```
tail
```


```
glimpse
```


```
str
```


```
skimr::skim()
```

---

# Veriyi değiştirme

## Veriyi kod ile değiştirelim

## Veriyi eklentilerle değiştirme

![](figures/change_data.png)

---


## RStudio aracılığıyla recode

*questionr* paketi kullanılacak

![](figures/level_recode.png)

---



https://juba.github.io/questionr/articles/recoding_addins.html


![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_1.png)


---

![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_2.png)


---

![](https://raw.githubusercontent.com/juba/questionr/master/resources/screenshots/irec_3.png)


---

# Basit tanımlayıcı istatistikler

```
summary()
```

```
mean
```

```
median
```

```
min
```

```
max
```

```
sd
```

```
table()
```


```{r descriptive, echo=TRUE, include = TRUE, fig.show='animate', aniopts='controls'}
library(readr)
irisdata <- read_csv("data/iris.csv")

jmv::descriptives(
    data = irisdata,
    vars = "Sepal.Length",
    splitBy = "Species",
    freq = TRUE,
    hist = TRUE,
    dens = TRUE,
    bar = TRUE,
    box = TRUE,
    violin = TRUE,
    dot = TRUE,
    mode = TRUE,
    sum = TRUE,
    sd = TRUE,
    variance = TRUE,
    range = TRUE,
    se = TRUE,
    skew = TRUE,
    kurt = TRUE,
    quart = TRUE,
    pcEqGr = TRUE)
```

---

```{r scatter, echo=TRUE, include=TRUE}
# install.packages("scatr")

scatr::scat(
    data = irisdata,
    x = "Sepal.Length",
    y = "Sepal.Width",
    group = "Species",
    marg = "dens",
    line = "linear",
    se = TRUE)

```

## summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
# library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
freq(iris$Species, report.nas = FALSE, style = "rmarkdown", omit.headings = TRUE)
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
with(tobacco, print(ctable(smoker, diseased), method = 'render'))
```


```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
with(tobacco, 
     print(ctable(smoker, diseased, prop = 'n', totals = FALSE), 
           omit.headings = TRUE, method = "render"))
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
summarytools::descr(iris, style = "rmarkdown")
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
      omit.headings = TRUE, style = "rmarkdown")
```



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
view(dfSummary(iris))

```


![](figures/dfsummary.png)



```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
dfSummary(tobacco, plain.ascii = FALSE, style = "grid")
```


```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
# First save the results
iris_stats_by_species <- by(data = iris, 
                            INDICES = iris$Species, 
                            FUN = descr, stats = c("mean", "sd", "min", "med", "max"), 
                            transpose = TRUE)

# Then use view(), like so:
view(iris_stats_by_species, method = "pander", style = "rmarkdown")
```

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
view(iris_stats_by_species)
```

![](figures/DescriptiveStatistics.png)

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr, 
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
BMI_by_age <- with(tobacco, 
                   by(BMI, age.gr, descr,  transpose = TRUE,
                      stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
tobacco_subset <- tobacco[ ,c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)
view(freq_tables, footnote = NA, file = 'freq-tables.html')
```

---

```{r, include=TRUE, comment=NA, prompt=FALSE, cache=FALSE, echo=TRUE, results='asis'}
what.is(iris)
```

---

```{r}
freq(tobacco$gender, style = 'rmarkdown')
```

---

```{r}
print(freq(tobacco$gender), method = 'render')
```




---

## Grafikler

```{r}
library(ggplot2)
library(mosaic)
mPlot(irisdata)
```

---

```{r}
ctable(tobacco$gender, tobacco$smoker, style = 'rmarkdown')
```

---

```{r}
print(ctable(tobacco$gender, tobacco$smoker), method = 'render')
```

descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')

---



<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Here, building up a <a href="https://twitter.com/hashtag/ggplot2?src=hash&amp;ref_src=twsrc%5Etfw">#ggplot2</a> as slowly as possible, <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a>.  Incremental adjustments.  <a href="https://twitter.com/hashtag/rstatsteachingideas?src=hash&amp;ref_src=twsrc%5Etfw">#rstatsteachingideas</a> <a href="https://t.co/nUulQl8bPh">pic.twitter.com/nUulQl8bPh</a></p>&mdash; Gina Reynolds (@EvaMaeRey) <a href="https://twitter.com/EvaMaeRey/status/1029104656763572226?ref_src=twsrc%5Etfw">August 13, 2018</a></blockquote><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>


---


[![](https://raw.githubusercontent.com/dreamRs/esquisse/master/man/figures/esquisse.gif)](https://github.com/dreamRs/esquisse)


<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Dreaming of a fancy <a href="https://twitter.com/hashtag/Rstats?src=hash&amp;ref_src=twsrc%5Etfw">#Rstats</a> <a href="https://twitter.com/hashtag/ggplot?src=hash&amp;ref_src=twsrc%5Etfw">#ggplot</a> <a href="https://twitter.com/hashtag/dataviz?src=hash&amp;ref_src=twsrc%5Etfw">#dataviz</a> but still scared of typing <a href="https://twitter.com/hashtag/code?src=hash&amp;ref_src=twsrc%5Etfw">#code</a>? <a href="https://twitter.com/_pvictorr?ref_src=twsrc%5Etfw">@_pvictorr</a> esquisse package has you covered <a href="https://t.co/1vIDXcVAAF">https://t.co/1vIDXcVAAF</a> <a href="https://t.co/RlTkptnrNv">pic.twitter.com/RlTkptnrNv</a></p>&mdash; Radoslaw Panczak (@RPanczak) <a href="https://twitter.com/RPanczak/status/1047019588658040832?ref_src=twsrc%5Etfw">October 2, 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>










---

# Rcmdr

```
library(Rcmdr)

Rcmdr::Commander()

```


- A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/


---

# jamovi

https://www.jamovi.org/

![![](https://www.jamovi.org/assets/main-screenshot.png)](https://www.jamovi.org/)


https://blog.jamovi.org/2018/07/30/rj.html

![![](https://blog.jamovi.org/assets/images/rj.png)](https://blog.jamovi.org/2018/07/30/rj.html)

---

# Sonraki Konular

- RStudio ile GitHub
- Hipotez testleri
- R Markdown ve R Notebook ile tekrarlanabilir rapor


---

# Diğer kodlar

- Diğer kodlar için bakınız: [https://sbalci.github.io/](https://sbalci.github.io/)


---

# Geri Bildirim

- Geri bildirim için tıklayınız: _[Geri bildirim formu](https://goo.gl/forms/YjGZ5DHgtPlR1RnB3)_



R ile analize başlarken¹

Derleyen Serdar Balcı, MD, Pathologist

2018-10-03

1 R yükleme

1.1 R-project

1.2 RStudio

1.2.1 RStudio eklentileri

1.3 X11

1.4 Java OS

2 R paketleri

2.1 Neden paketler var

2.2 Paketleri nereden bulabiliriz

2.3 R için yardım bulma

2.4 R paket yükleme

3 R studio ile proje oluşturma

4 RStudio ile veri yükleme

4.1 Excel

4.2 SPSS

4.3 csv

5 Veriyi görüntüleme

6 Veriyi değiştirme

6.1 Veriyi kod ile değiştirelim

6.2 Veriyi eklentilerle değiştirme

6.3 RStudio aracılığıyla recode

7 Basit tanımlayıcı istatistikler

7.1 summarytools

7.1.1 Frequencies

Cross-Tabulation / Row Proportions

7.1.2 Data Frame Summary

7.1.3 Descriptive Statistics

7.1.4 Descriptive Statistics

tobacco_subset$age.gr

tobacco_subset$smoker

Frequencies

7.2 Grafikler

8 Rcmdr

9 jamovi

10 Sonraki Konular

11 Diğer kodlar

12 Geri Bildirim

R ile analize başlarken1

Derleyen Serdar Balcı, MD, Pathologist

2018-10-03

1 R yükleme

1.1 R-project

1.2 RStudio

1.2.1 RStudio eklentileri

1.3 X11

1.4 Java OS

2 R paketleri

2.1 Neden paketler var

2.2 Paketleri nereden bulabiliriz

2.3 R için yardım bulma

2.4 R paket yükleme

3 R studio ile proje oluşturma

4 RStudio ile veri yükleme

4.1 Excel

4.2 SPSS

4.3 csv

5 Veriyi görüntüleme

6 Veriyi değiştirme

6.1 Veriyi kod ile değiştirelim

6.2 Veriyi eklentilerle değiştirme

6.3 RStudio aracılığıyla recode

7 Basit tanımlayıcı istatistikler

7.1 summarytools

7.1.1 Frequencies

Cross-Tabulation / Row Proportions

7.1.2 Data Frame Summary

7.1.3 Descriptive Statistics

7.1.4 Descriptive Statistics

tobacco_subset$age.gr

tobacco_subset$smoker

Frequencies

7.2 Grafikler

8 Rcmdr

9 jamovi

10 Sonraki Konular

11 Diğer kodlar

12 Geri Bildirim

R ile analize başlarken¹